import pandas as pd
from mlxtend.preprocessing import TransactionEncoder
from mlxtend.frequent_patterns import apriori, association_rules

#读取菜品数据
df=pd.read_excel('D:/lyj/test2/xm2/餐厅数据.xlsx')

#转换菜品数据格式
trsation=df['菜品'].str.split(',').to_list()

# print(trsation)

#标准化数据
ts=TransactionEncoder()
te_ary=ts.fit(trsation).transform(trsation)
df_enecoded=pd.DataFrame(te_ary,columns=ts.columns_)
# print(df_enecoded)

#使用apriori进行关联规则挖掘
frequent_itemsets = apriori(df_enecoded,min_support=0.1,use_colnames=True)
frequent_itemsets.sort_values(by='support',ascending=False, inplace=True)

# #选择2项集
# print(frequent_itemsets[frequent_itemsets.itemsets.apply(lambda x:len(x) - 2)])
rules=association_rules(frequent_itemsets,metric='confidence',min_threshold=0.1)
#筛选有效规则
effective = rules[
    (rules['lift'] > 1) & (rules['confidence']>0.1) 
    ].sort_values(by=['lift','confidence'],ascending=False)           
print(effective[['antecedents','consequents','support','confidence','lift']])

# 可视化查看关联规则
import matplotlib.pyplot as plt
import networkx as nx
# 设贸字体
plt.rcParams['font.sans-serif']=['Simhei']
plt.rcParams['axes.unicode_minus']= False
#关联关系可视化
G=nx.DiGraph()
for _, row in effective.iterrows():
    G.add_edge(','.join(list(row['antecedents'])),
               ','.join(list(row['consequents'])),
               weight=row['lift']
    )
plt.figure(figsize=(12,8))
pos=nx.spring_layout(G)
nx.draw(G, pos,
        with_labels=True,
        edge_color=[G[u][v]['weight'] for u, v in G.edges()],
        width=2.0,
        edge_cmap=plt.cm.Blues)
plt.show()